# Module 6 “Normal Values”: How are Normal Reference Ranges Established?

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Module 6 “Normal Values”: How are Normal Reference Ranges Established?

Doctor, was my test normal?

Reference Ranges O Comparison of a patient’s laboratory test result versus a reference or “normal” range is an important aspect of medical decision making O Reference Ranges are required by professional accreditation and regulatory standards

Reference Ranges O Laboratory directors determine and evaluate reference ranges reported with all test results O In most cases, a “normal range” is used as the test’s “reference range”. O For some analytes, the reference range is defined as “less than” or “greater than” a certain value O Example: total cholesterol: <200mg is desirable

Establishing Reference Ranges O When selecting the decision threshold or cutoff value (a limit above or below which a patient is considered affected by a particular disease, i.e., normal or abnormal), a variety of methods can be used.

Gaussian Distribution O If test results from a normal healthy patient population fall into a bell-shaped, Gaussian, normal distribution, the central 95% is usually used as the test’s normal range. O For many (but not all) tests, this is how the range of tests results for normal healthy individuals is determined.

Some Normals are Abnormal …… and Vice Versa O The normal range encompasses the mean plus or minus two standard deviations or, again, about 95% of normal, healthy individuals’ test results. O However, 5% (roughly 1 out of 20) normal healthy patients may be outside the cutoff value. O Roughly 2.5% of normal people can be expected to have a result below and roughly 2.5% of normal people can be expected to have a result above the reported normal range. O This situation is encountered with almost all tests. O This is because the distribution of tests results from normal, healthy individuals overlaps with the distribution of test results from sick patients with the relevant disease.

Two Populations of Results O Theoretically, the better tests minimize this overlap between the distribution of normal and abnormal test results. O An ideal test would have no overlap at all and could perfectly discriminate between a normal and abnormal test result. O Lab experts continue to look for at least one test like this …….

Non-Gaussian Distributions O For non-Gaussian distributions, lab directors can use other nonparametric techniques to establish reference range limits O Example: set upper and lower limits of normal to include 95% of the population after all of the test results have been transformed into logarithms taking the central 95% of the transformed data.

Where’s the Threshold or Cut-off? O Ultimately, where the lab places the limits (threshold or cut-off) on a normal or reference range determines what level of result is considered “normal” or “abnormal”.

O In the next slide, the values for the concentration of a hypothetical analyte were determined in a group of 200 healthy persons and in a group of 50 diseased persons. The raw data for the group were fitted to Gaussian distributions. O A through D represent possible cutoff values that could be used to classify subjects based on the analyte values.

Analyte Value (units) Frequency 10 20 30 4050 60 70 80 90 100 110120 130 Healthy Persons Diseased Persons ABCD Cutoffs Distribution of a Test Result in Healthy (n=200) and Diseased (n=50) Persons

O What would be the advantage(s) of selecting A as the cut off value for “normal”? O All patients with the disease would have a positive test result O What would be the advantage(s) of selecting D as the cut-off value? O All healthy patients would have a negative test result

O What would be the disadvantage(s) of selecting D as the cut off? O Persons with the disease may not be diagnosed O What would be the disadvantage(s) of selecting A as the cut-off? O Patients who do not have the disease would be classified as having an “abnormality”

Consider the implications if this were a screening test for cancer…. Image by Theresa Kristopaitis, MD

Recall the Definitions of Sensitivity and Specificity O Sensitivity is the ability of a test to detect disease O Proportion of persons with disease in whom the test is positive O Specificity is the ability to detect the absence of disease O Proportion of persons without disease in whom the test is negative

Predictive Value Grid Test ResultDisease or SickNo Disease (Normal, Healthy) Positive Result* True PositivesFalse Positives Negative Result*False NegativesTrue Negatives TOTAL *“positive” usually refers to a test being abnormal, “negative” usually refers to normal

Does this Grid Reflect Cut-off Value A or D? Test ResultDisease or SickNo Disease (Normal, Healthy) Positive Result* True Positives 50 False Positives 25 Negative Result*False Negatives 0 True Negatives 175 TOTAL50200

Answer – Cutoff Value A The sensitivity of the test would be 100% HOWEVER The specificity of the test would be 87%

O Sensitivity and specificity therefore are not fixed characteristics of a test and must be calculated for each cutoff chosen O When a test cutoff is altered, an inverse relationship between sensitivity and specificity is noted

Where’s the Threshold or Cut-off? O To reiterate, where the lab places the threshold or cut-off on a normal or reference range determines what level of result is considered “normal” or “abnormal”. O It also affects the distribution of values and how they are tallied in the predictive value grid and resultant diagnostic value of a test O It affects the care of patients and may have serious implications

Congratulations! You have completed the “Introduction to Laboratory Medicine” modules!

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